Abstract
Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
| Original language | English |
|---|---|
| Pages (from-to) | 34-58 |
| Number of pages | 25 |
| Journal | IEEE transactions on pattern analysis and machine intelligence |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2002 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics
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}
Detecting faces in images : A survey. / Yang, Ming Hsuan; Kriegman, David J.; Ahuja, Narendra.
In: IEEE transactions on pattern analysis and machine intelligence, Vol. 24, No. 1, 01.01.2002, p. 34-58.Research output: Contribution to journal › Article
TY - JOUR
T1 - Detecting faces in images
T2 - A survey
AU - Yang, Ming Hsuan
AU - Kriegman, David J.
AU - Ahuja, Narendra
PY - 2002/1/1
Y1 - 2002/1/1
N2 - Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
AB - Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
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UR - http://www.scopus.com/inward/citedby.url?scp=0036223025&partnerID=8YFLogxK
U2 - 10.1109/34.982883
DO - 10.1109/34.982883
M3 - Article
AN - SCOPUS:0036223025
VL - 24
SP - 34
EP - 58
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
SN - 0162-8828
IS - 1
ER -